Data-driven tools are needed to inform individualized treatment decisions for people with type 2 diabetes (T2D). To show how treatment might be individualized, an interactive outline tool was developed to predict treatment outcomes. Individualized predictions were generated for change in HbA1c and body weight after initiation of newer antidiabetes drugs recommended by current guidelines. These predictions were based on data from randomized controlled trials of glucose-lowering drugs. The data included patient demographics and clinical characteristics (sex, age, body mass index, weight, diabetes duration, HbA1c level, current diabetes treatment and renal function). Predicted outcomes were determined using prespecified statistical models from original trial protocols and estimated coefficients for selected baseline characteristics. This prototype illustrates how evidence-based individualized treatment might be facilitated in the clinic for people with T2D. Further and ongoing development is required to improve the tool's prognostic value, including the addition of disease co-morbidities and patient-orientated outcomes. Patient engagement and data-sharing by sponsors of clinical trials, as well as real-world evidence, are needed to provide reliable predicted outcomes to inform shared patient–physician decision-making.